Targeted Sentiment to Understand Student Comments

Charles Welch, Rada Mihalcea


Abstract
We address the task of targeted sentiment as a means of understanding the sentiment that students hold toward courses and instructors, as expressed by students in their comments. We introduce a new dataset consisting of student comments annotated for targeted sentiment and describe a system that can both identify the courses and instructors mentioned in student comments, as well as label the students’ sentiment toward those entities. Through several comparative evaluations, we show that our system outperforms previous work on a similar task.
Anthology ID:
C16-1233
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2471–2481
Language:
URL:
https://aclanthology.org/C16-1233
DOI:
Bibkey:
Cite (ACL):
Charles Welch and Rada Mihalcea. 2016. Targeted Sentiment to Understand Student Comments. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2471–2481, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Targeted Sentiment to Understand Student Comments (Welch & Mihalcea, COLING 2016)
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PDF:
https://aclanthology.org/C16-1233.pdf